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However deep nets suffer from the drawback that they require large training times and mul- tifarious parameters that need to be hand tuned for optimal performance.
In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets.
Convolutional neural networks have been proven to have strong feature representation ability; however, they often require large training samples and high computation that are infeasible for real-time finger vein verification.
It also helped to demonstrate that the methodology does not require large training sets to work.
These techniques, however, require large training data and employ characters as units of recognition rather than ligatures or words.
Supervised machine learning for prediction of chemical class membership based on an existing structural hierarchy is an interesting option, but would require large training sets of chemicals that are already classified.
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However, in general, better models require larger training sets, which in turn require more CPU hours.
However, building of ANN surrogate models usually requires large training datasets.
This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora.
However, the predication accuracy of the neural network approaches is not high and the application of neural networks requires large training sets, which may be infeasible in practice.
However, the development of accurate prediction models requires large training sets of genotyped and phenotyped individuals.
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